Firm Characteristics and Employee Entrepreneurs’ Choice of ... · having the same skills...
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Firm Characteristics and Employee Entrepreneurs’ Choice
of Cofounders and Early Employees
Jing Chen
Copenhagen Business School
March 2013
Abstract
In the early stage of a startup, employees and founders are more integrated as one
workgroup. The boundary between their functional roles is not as clear as that in more
established firms. Given this special relationship between early employees and founders,
this paper revisits the view of functional diversity in explaining founding team formation,
taking in account the interplay between founding team members and early employees.
Using data from Statistics Denmark, I examine how a former coworker’s likelihood of
becoming a cofounder or an early employee is responsive to the skill match between him
and the entrepreneur as being complementary or substitutive. I find that a coworker is
more likely to become a cofounder if he has different skills from the entrepreneur, and to
become an early employee if they have the same skills. This result indicates the relevance
of common knowledge sharing between early employees and entrepreneurs, but not so
much among founding team members.
JEL Classification Codes: D21, D22, J62, L26
Keywords: Early employees, founding team, coworker, diversity.
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1. Introduction
This paper examines the distinction between founding partners and early employees, and
how this difference is perceived by entrepreneurs in their early recruitment decision.
With whom should an entrepreneur cofound a business? Who should an entrepreneur
hire as an early employee? What distinguishes a competent cofounder from a qualified
early employee? These are the questions that are central to the paper. Specifically, I
focus on the difference in selection criteria of co-founders and early employees, with
respect to skill complementarity between them and entrepreneurs.
There has been an extensive literature on founding team composition and its relationship
with business performance. At the core of these discussions is the question of whether
diversity is preferable to homophily for the structure of founding team, while diversity is
measured along different dimensions, such as demography, functional skills, industry
experience and prior employment affiliation (e.g., Pelled et al. 1999, Ruef et al. 2003,
Kor 2003, Beckman 2006, Beckman and Burton 2008, Eesley et al. 2013). The empirical
evidence on individual functional and industry experience tend to support a positive
relationship between founding team diversity and firm performance (Eisenhardt and
Schoonhoven 1990, Beckman et al., 2007, Fern et al. 2010), although the literature also
shows that work groups, more generally, tend to perform better when members have
similar and general, as opposed to different and specialized, knowledge (Rulke and
Galaskiewicz 2000, Liang 1994).
A potential issue with these studies of founding team structure is that founding team
members are often treated as independent of the rest of workforce in a startup. However,
in practice there is not always a clear line drawn between founders and those early
employees, in terms of their functional roles in the startup. Compared to a vast amount
of literature on entrepreneurial founding teams, few studies have explored this group of
early workforce who joined startups. Related issues, such as the sources of early
employees, their group composition, and its dynamic pattern, remain largely
underexplored in the entrepreneurship literature. However, because early employees
interact closely with founders, it raises a question of whether the observed structure of
founding team is influenced by skill composition of early employees. The rationale
underlying this hypothesis relies on the pros and cons of team diversity that have been
discussed in the literature. On the one hand, diversity is seen as a way to increase firm's
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competency with more accesses to resources, more complete functional structure, and a
faster process of problem solving (Eisenhardt and Schoonhoven 1990). On the other hand,
diversity could also cause inefficiency by creating higher barriers of idea communication
and knowledge distribution (See Rulke and Galaskiewicz 2000 for a review). How do
these pros and cons affect entrepreneurs' choice of founding team structure? By focusing
on founding team itself, previous studies suggest that entrepreneurs can only choose to
favor either competency or efficiency. In this paper, I argue that entrepreneurs could be
doing both by differentiating skill structures of founder and early employees. An
interesting question is whether entrepreneurs tend to place more values on founding
team diversity while improving task-specific communication through hiring employees
with the same skills as them, or they are more likely to select cofounders similar to them
while hiring employees with diversified skills to execute different functional roles in the
early stage of business operation.
To explore these two possibilities, I construct a dataset based on an employer-employee
matched database. The dataset links a cohort of entrepreneurs to all the coworkers from
their previous workplaces. I estimate the differing effects of having the same occupational
skills as the entrepreneur on the likelihood that the coworker becomes a cofounder as
opposed to becoming an early employee. By confining the analysis in the context of
coworker relationship, I attempt to identify all individuals who are at risk of joining the
entrepreneur’s new venture. The drawback of this design is that entrepreneurs may also
choose to hire people outside the coworker network. However, there are several reasons
why coworkers could be the most likely source for entrepreneurs to look for founding
partners and employees. First, entrepreneurs generally face network and geographic
constraints (Ruef et al. 2003). Aside from family members and friends, coworkers form
another group of social contacts with whom prospective entrepreneurs have more
opportunities to develop a strong interpersonal relationship. Second, coworker
relationship builds on common knowledge, language, and routines at work (Nahapiet and
Ghoshal 1998), which provides two advantages of involving coworkers in the process of
new business creation. On the one hand, because of shared working experience, coworker
relationship is more effective in stimulating business ideas and making decisions than
family ties, regular friendship or strangerhood (Eisenhardt and Schoonhoven 1990,
Beckman 2006). Indeed we have seen ample amount of evidence in cases where former
coworkers created employee spinoffs together (Klepper 2001, Agarwal et al. 2011). On
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the other hand, recruiting former coworkers in s startup also alleviates the problem with
information asymmetry (Akerlof 1970). Lastly, working with former coworkers could also
avoid issues such as nepotism that often plague family businesses (Bertrand and Schoar
2006). For these reasons, this study focuses on entrepreneurs’ prior coworkers as
potential candidates of cofounders and early employees, and examines how the type of
skill match between them and the entrepreneurs affects a specific recruiting outcome.
The main finding in the data shows that having different occupational skills from the
entrepreneur increases the likelihood that a former coworker becomes a cofounder, while
having the same skills increases the likelihood of him becoming an early employee. This
result suggests that when selecting cofounders, entrepreneurs tend to place more value
on functional diversity than commonality of skills. In contrast, entrepreneurs emphasize
more homogeneity and probably communicative efficiency when recruiting early
employees.
Another interesting finding on the side is that the effect of skill matching on the
entrepreneur's recruiting outcome (for both cofounders and early employees) is
responsive to the size of the entrepreneurs’ previous workplace. Having the same skill as
then entrepreneur has a smaller positive effect on the likelihood that a coworker becomes
an early employee if the prior workplace is larger. But the positive effect of skill diversity
on the probability of a coworker becoming a cofounder is increasing with firm size. One
plausible explanation for these contrasting results is the distinct nature of jobs defined in
small and large firms, which may affect skill composition among entrepreneurs’
coworkers. Thus, workplace characteristics may influence employees' propensity to enter
entrepreneurship by creating advantage or constraints of finding cofounders and early
employees in the coworker network.
The remainder of the paper is organized as follows. In the next section, I describe in
details the data sources, the construction of dataset and the key variables, followed by
descriptive summaries. Section 3 presents the main results, and section 4 concludes.
2. The Data
2.1 Data Sources
To examine skill matching between entrepreneurs, founding partners and early
employees, I need a comprehensive dataset that meets three criteria. First, I need to
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identify all individuals in the startups and their positions, so as to distinguish a founder
from an early employee. Second, I need detailed information about each individual’s
occupational skills so that I can measure skill complementarity. Third, I need to identify
all potential co-founders and early employees who might have joined the startups, and
provide information about their occupational skills. In this empirical setting, the group of
individuals who were at risk of joining the startups is confined to entrepreneurs’
coworkers at the previous workplace. To construct such a dataset, I employ three
databases maintained by Statistics Denmark.
The Entrepreneur Database records all new businesses created in Denmark each year
from 1996 to 2006. A unique identifier is assigned to each business by Statistics Denmark.
Using this unique identifier, The Entrepreneur Database can be linked to The Firm
Database, which consists of employment information about all workers (full-time and
part-time) at all firms existing in Denmark from 1995 to 2008. Based on a variable that
specifically describes an individual’s position in the firm, it is possible to identify whether
a person was a founder or an early employee. The Firm Database can be further linked
to the Integrated Database for Labor Market Research (IDA), which is an employer-
employee matched panel from 1980 to 2008. Firms included in this database must have
at least one person working there as his primary occupation. The IDA database allows
me to track an individual’s previous employer prior to starting or joining a startup,
identify all his coworkers at the previous workplace, and collect information about their
occupational skills.
A somewhat controversial issue of conducting entrepreneurship research using data from
Denmark is whether the Danish context is too peculiar to provide more general insights
that are applicable to other settings. In fact, despite its small economy, Denmark has
some of Europe's highest levels of early-stage entrepreneurial activity. The nascent
entrepreneurship rate in 2011 is 3.1% in Denmark, which is lower than 8.3% in the US,
but comparable with 3.4% in Germany and 4.7% in UK.1 Between 2001 and 2002, the
time adjacent to the observation window of this analysis, the average new firm birth rate
1 GEM 2011 Global Report. Nascent entrepreneurs are defined by GEM as “individuals between the ages of 18 and 64 years, who have taken some action toward creating a new business in the past year. To qualify for this category, these individuals must also expect to own a share of the business they are starting and the business must not have paid any wages or salaries for more than three months.”
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is 9% in Denmark, compared to 7.7% in Germany, 10.4% in UK, and 11.5% in the US.2
The so-called Flexicurity system adopted in Danish labor market provides employers
with large flexibility regarding hiring and firing employees, while in the meantime offers
workers generous unemployment benefits and incentives to return to work. Like most
developed countries, Danish government has implemented a wide range of public
programs to promote entrepreneurship. Administrative burdens for entrepreneurship are
also kept at the minimum level. In general, Denmark has a favorable environment for
new business creation. The economy has its peculiar aspects, such as the high taxation
which may have a negative impact on entrepreneurship, but there are also many
commonalities between Denmark and other developed European countries with respect
to the overall conditions for entrepreneurship, including business environment,
government regulation and entrepreneurship culture.
2.2 Dataset Construction
2.2.1 Startup, Founder, and Early Employee
From the Entrepreneur Database, I collect 1,690 Danish startups created in 1999. Before
this year, accounting information was not complete for some industries. All these firms
can be matched to the IDA database, indicating that at least one person was working in
the firm as his primary occupation. Because it is an employer-employee matched
database, the IDA allows me to identify all individuals working (full-time or part-time)
at these firms in 1999, as well as collect their demographic, prior and current
employment information.
To distinguish founders from early employees, I use a variable available in the Firm
Database, which describes a person’s job position in the firm. A person is defined as a
founder if he meets one of the following four criteria: (1) his job position is classified as
self-employed or employer; (2) his position is top manager in a business with fewer than
four employees; (3) he works in a business with fewer than four employees, where no one
falls into the job position categories listed in (1) and (2); and (4) the person does not
meet the first three criteria, but he is the one who registered the business and is
2 GEM 2005 Global Report. New firm birth rate is defined by GEM as the number of expected births per 100 of existing firms.
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currently working at the business.3 The rest of individuals at a startup who do not meet
any of the four criteria are treated as early employees.
Based on this definition, I identify total 2,120 (co)founders and 3,645 early employees
who were working for the original 1,690 startups in 1999. These businesses are in general
very small. The average number of people working in the business is 3.4. Thirty-eight
percent of these businesses (642) did not hire any employee in the founding year.
2.2.2 Identifying coworkers at the previous workplace
The IDA database provides comprehensive employment records for each individual
included in the data, which can be traced back to 1980. To simplify the analysis, I focus
on their most recent employer in 1998 or 1997 (if not employed in 1998). Another reason
is that entrepreneurs are likely to make better assessment of skills of the most recent
coworkers. A brief exploration of the data shows that 6.6 percent of the businesses in the
data were co-founded by people with joint recent work experience at the same plant, and
twenty-three percent hired an early employee with whom at least one cofounder shared
the same workplace in recent years.
To identify the pool of potential founding partners and early employees at entrepreneurs’
previous workplace, I take advantage of the employer-employee matched feature of the
database and collect all the individuals who worked at the same plant as the
entrepreneurs in 1998 or 1997. There are 227 cases where none of the founders in the
business had an employment record in the past two years prior to founding. After
removing those cases, I identify 1,657 firms and 1,772 plants, where 1,954 (out of 2,120)
(co)founders in the sample worked in 1998 or 1997. Table 1 shows that thirty-five
percent of these parent firms are related to the wholesales and retails industry. Thirty
percent is evenly distributed between manufacturing and construction. Public services
and knowledge intensive business services account for another twenty percent of
observations. By comparison, startups spawned by these firms are most likely to
concentrate in wholesale and retail, construction and knowledge intensive business
services, but less likely in manufacturing or public and personal services.
3 The first three criteria were first suggested by Sørensen (2007).
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There are 177,793 individuals working at these plants around the same time as the
entrepreneurs. Among them, 1,064 left the plants and joined various startups in 1999 as
early employees. The majority of these people (982) joined startups created by their
coworkers, and the rest became early employees at startups founded by people who were
not from the same plant.
[Insert Table 1 about here]
2.2.3 Occupational skills
To compare the occupational skill of entrepreneurs with that of their coworkers, I
restructure the dataset so that, for each individual, there is a match between him and a
startup founded by his coworker in 1999, which he could possibly join as a cofounder or
an early employee. If more than one employee startup was spawned from his workplace,
I create a match between the focal individual and each of these businesses. Each
observation on the match includes occupational information for the focal individual and
for all the founders in the startup who were his coworkers at the same plant. I measure
occupational skills using the four-digit occupation code, which is based on the Danish
version of the international standard classification of occupations.4
The dataset is structured at the individual level. Depending on how many startups were
spawned in 1999 from a plant, there could be several observations on each focal
individual who worked at this plant. If the focal individual himself cofounded a startup
with another coworker, this could result in a duplicated observation on the match. Table
2 illustrates the structure of the dataset with an artificial example. In this example,
there are two startups created in 1999 by former employees at workplace W1. The first
startup, N001, was solely founded by employee P2. The second startup, N002, was
cofounded by employees P3 and P5. For each individual who was working at workplace
W1 around the same time as the three entrepreneurs (in 1998 or 1997), there are two
observations on the match between him and the two startups, respectively. However, if
the person is the only founder of a startup who worked at workplace W1, the
4 The reason for using the four-digit rather than a more detailed six-digit code is that the nuance between close categories at the six-digit level rests more on the same occupation associated with different industries. For instance, occupation code 3111 refers to technicians in science. At the six-digit level, this occupation category is further classified as technician working in geology, chemical processes, electrical systems and equipment, machinery, etc.
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observation that links him to his own startup is removed from the sample (row 3).
Moreover, if the startup has two founders who worked at the same plant before, there is
a duplicated observation on the matching between them (rows 6 and 8). This duplicate
is not removed from the data, because the empirical analysis focuses on employment
outcome of each focal individual and his employment outcome, controlling for his
demographic and employment characteristics.
[Insert Table 2 about here]
I compare a focal individual’s occupational skill with all (remaining) founders in the
startup who were his coworkers at the previous workplace. A person’s occupational skill
is considered being complementary to the founding team if none of the (remaining)
founders shares with him the same occupation code. Otherwise, the person is considered
having the same skill as one or more cofounders.
2.2.4 Occupational choice
I create three categories for individuals’ employment options in 1999. These options
include (1) starting a business with coworkers; (2) joining a coworker’s startup as an
early employee; and (3) all the others, such as staying at the same workplace, starting a
business without a coworker, joining startups that were not founded by coworkers, etc.
It is not surprising that in the sample, 99 percent of observations fall into the third
category, and the majority of them did not join or start any startup in 1999. There are
1,954 individuals who cofounded a business with previous coworkers, and 982 individuals
who joined startups created by their coworkers as early employees.
Table 3 reports descriptive statistics by individuals’ occupational choice. There is no
significant demographic difference across the three groups of people. But compared to
those who became early employees, individuals who became founders on average earn
more but have shorter tenure at the previous workplace. They are also more likely to be
employers, have entrepreneurial experience, and come from larger firms.
[Insert Table 3 about here]
3. Empirical Analysis
3.1. Skill Match and the Choice of Cofounders versus Early Employees
(1) The Baseline Specification
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The likelihood that an entrepreneur recruits a former coworker as a cofounder or an
early employee depends on an array of factors, including the coworker’s skill quality, his
relationship with the entrepreneur, his preference for working for startups, as well as the
entrepreneur’s preference for working with former coworkers. The entrepreneur's
recruiting outcome can be expressed as the following baseline equation:
(1)
is the outcome variable, indicating whether or not a former co-worker i join
entrepreneur j as a co-founder or an early employee. is an indicator of skill similarity
between coworker i and entrepreneur j. It equals one if the coworker has the same skills
as the entrepreneur, and zero if otherwise. is the size of their previous workplace w.
This variable expects to capture two things. One is how closely entrepreneur j and
coworker i worked together at workplace w, and the other is coworker i’s unobserved
preference for working for startups, given the possible difference between small and large
firm employees with respect to entrepreneurial propensity (Elfenbein et al. 2010). The
covariates include co-worker i’s demographic characteristics such as age, gender, race,
marital status, number of children under 18, education, self-employment experience, as
well as his employment information at workplace w, such as tenure, position, and hourly
wage.
(2) The Interaction between Firm Size and Skill Similarity
Skill similarity between entrepreneur j and a coworker i affects the likelihood of coworker
i becoming a cofounder or an early employee in two ways. On the one hand, if skills
required for a cofounder significantly differ from those required for an early employee, we
should expect to see the contrasting effects of having the same skills as the entrepreneur
on the likelihood that a coworker becomes a cofounder, compared to becoming an early
employee. This effect comes through the level effect of skill similarity, , estimated in
equation (1).
On the other hand, skill similarity may not affect these recruiting outcomes in the same
way across all workplaces. The differences could be driven by three possible mechanisms.
First, small and large firm employees may differ in their preference and skills for working
for small entrepreneurial ventures (Elfenbein et al 2010). Small firm employees are
perceived as having more entrepreneurial-oriented personalities and having more
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diversified skill sets, which may increase their propensity to join entrepreneurial ventures
(Lazear 2005, Sørensen 2007, Gompers et al 2005). This difference between small and
large firm employees may strengthen or attenuate the effect of skill similarity on the
likelihood of recruiting coworkers in a startup as cofounders or early employees.
The second mechanism focuses on the information effect of firm size. As the size of
workplace increases, the relatedness of the entrepreneur to a focal coworker declines.
This means that entrepreneurs obtain better information about former coworkers' skills if
their previous workplace is smaller. Thus, because of information asymmetry, firm size
may diminish the effect of skill similarity on the likelihood of former coworkers being
recruited in a startup.
The third mechanism considers skill composition among coworkers, and its variation
with firm size. Because job categories in large firms are more narrowly defined than
small firms (Elfenbein et al. 2010), coworkers in large firms, who have close interactions
with each other at work, are more likely to share similar expertise in a specific field. In
contrast, small firm employees tend to engage in more broadly-defined job activities,
partly because the number of positions in each job category is more limited at firms with
smaller size. Thus, the network built by small firm employees is likely to consist of
coworkers with more diversified skills from each other, while large firm employees are
more likely to have coworkers with similar skills. For entrepreneur j, the fraction of his
former coworkers with the same skills as him is expected to increase with the size of his
previous workplace. If skill similarity has a positive impact on the likelihood of coworker
i being the best choice for a position in entrepreneur i's startup, this effect would become
smaller in a larger workplace because there would be more candidates with similar skills
to the entrepreneur. In contrast, if skill diversity has a positive effect on the likelihood
that coworker i joins the startup, increasing the size of workplace would further enlarge
this effect, as there are fewer coworkers in entrepreneur j’s network who have diversified
skills from the entrepreneur.
To take these effects into account, I estimate the following full equation with the
interaction between skill similarity and the size of entrepreneurs' previous workplace
, measured by the number of employees in the firm before the departure of
entrepreneurs,
∗ (2)
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All other controls remain the same as in the baseline equation (1).
(3) Results
I am particularly interested in comparing skills required for cofounders and early
employees. I first estimate equations (1) and (2) using the regular logit model based on a
subsample of individuals who join a coworker’s startup as either a cofounder or an early
employee. There is an advantage of focusing on the restricted sample. Because
individuals included in the restricted sample become either a cofounder or an early
employee, I could partly control for the relatedness of the coworker to the entrepreneur,
and therefore minimize the relatedness effect of firm size. In the full sample, I estimate
equation (2) using the multinomial probit model. For all individuals in the sample, they
have three career choices in 1999: (1) join a coworker's new business as a cofounder, (2)
join a coworker's new business as an early employee, and (3) do not join any coworker's
business. The results are reported in table 4.
Column (1) presents two interesting baseline results. First, the positive coefficient
estimate of skill similarity suggests that a coworker is more likely to become an early
employee if he has the same occupational skill as the entrepreneur. Alternatively
speaking, skill dissimilarity increases the likelihood that a coworker becomes a cofounder.
The two opposing roles played by skill similarity in the selection of cofounders and early
employees suggest that even though the line between founders and early employees may
not be particularly clear sometimes, there is in fact distinction between them with
respect to the match of their skills to the entrepreneur's expertise. The results imply that
functional diversity is more emphasized in the selection of founding partners, while
homogeneity in skills is more preferred while entrepreneurs choose early employees. This
is probably because entrepreneurs could more easily communicate with employees about
how to perform a specific task if they share more common skills or technical knowledge.
[Insert Table 4 about here]
Column (1) also shows a strong and negative effect of previous workplace size on the
likelihood that coworker i becomes an early employee, relative to becoming a cofounder.
A similar relationship is evidenced in Table 3. At first glance, this result implies that
coworkers at small firms are more likely to become early employees while those at large
firms are more likely to become cofounders. This implication is at odds with the so called
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“the small firm effect” consistently documented in the literature, which describes a
negative relationship between firm size and the likelihood of employees becoming
entrepreneurs. However, it is worth noting that the analysis so far is based on a
restricted sample of either early employees or cofounders. Thus, the correct way to
interpret the negative coefficient of firm size is to focus on the effect of firm size on an
individual's likelihood of becoming a cofounder, compared to becoming an early employee.
The result suggests that although small firm employees on average are more likely to
become entrepreneurs, large firm employees are even less likely to join startups as early
employees than starting their own business. Simple tabulation statistics presented in
table 5 confirm this intuition. Dividing observations into five categories based on firm
size, the table shows a substantially higher percentage of small firm employees who
become early employees, compared to the fraction of large firm employees who make the
same occupational choice. This descriptive result is consistent with the notion that small
firm employees have higher preference for working in more entrepreneurial environment,
either as founders or early employees. Moreover, the result that coworkers at larger firms
are less likely to become early employees may also be driven by the fact that there are
fewer employee startups spawned by large firms, which otherwise could provide
alternative career opportunities for fellow coworkers.
[Insert Table 5 about here]
Column (2) reports the results after considering the interaction between firm size and
skill similarity. The estimated main effects of the two variables are quantitatively
comparable with those reported in column (1). The negative coefficient of the interaction
term suggests that the positive effect of skill similarity on a coworker’s likelihood of
becoming an early employee relative to being a cofounder is decreasing with firm size.
Since the information effect of firm size is rather trivial in a sample consisting of only
coworkers who become cofounders or early employees, the negative interaction effect
should be mainly attributed to the other two mechanisms discussed above. On the one
hand, large firm employees are less likely to join a coworker’s entrepreneurial firm as an
early employee. Thus, firm size mitigates the positive role of skill similarity in the
selection of early employees. On the other hand, as firm size increases, the entrepreneur
is likely to have more coworkers with whom he shares similar skills. This decreases each
individual coworker’s likelihood of being selected for the position. Both mechanisms
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suggest a reduction in the effect of skill similarity on the likelihood of a coworker
becoming an early employee. However, given the small magnitude of the interaction
effect, the net effect of skill similarity remains positive.
Additional results for individual demographic characteristics further show that a
coworker's likelihood of becoming an early employee rather than a cofounder is higher if
the coworker is female or older. The likelihood is also higher if the coworker has longer
tenure at the workplace or has lower hourly earnings. Moreover, individuals who are
employers at the workplace are less likely to join a former employee’s startup as an early
employee.
Columns (3) and (4) report the multinomial probit estimates of equation (2). Becoming
a cofounder is treated as the base outcome is. Consistent with the previous two columns,
skill diversity appears to be strongly and positively correlated with the likelihood that
coworker i becomes a cofounder. He is more likely to become an early employee or not
join up entrepreneur j's startup if they share the same occupation. The results for firm
size are also intuitive. Compared to being a cofounder, large firm employees are more
likely to not join a coworker's startup, and even less likely to become an early employee
in those startups. As firm size increases, there are more coworkers who have the same
skills as entrepreneur j. This reduces the positive effect of skill similarity on the
likelihood that coworker i becomes an early employee, but increases its positive effect on
the likelihood that coworker i does not join entrepreneur j's startup.
To summarize, the results reported in table 4 highlight two factors that influence how
entrepreneurs choose cofounders and early employees. One factor is skill match between
entrepreneurs and a potential candidate. Skill diversity is found to be more valuable for
a cofounder, while skill similarity is more preferable for an early employee. The other
factor is the size of prior employer. As firm size is related to the type of human capital
accumulated by employees, it may affect skill composition among coworkers. Small firm
employees are more likely to be surrounded by coworkers with diversified skills, while
large firm employees tend to have coworkers with similar skills. This distinction between
small and large firms provides an alternative way to examine the role of skill match in
selecting cofounders and early employees. If it is true that entrepreneurs choose
cofounders with diversified skills and hire coworkers with the same skills as them, we
would expect to see that entrepreneurs who worked for small firms are likely to have
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more former coworkers in the founding team because of their diversified skills, while
those who worked for large firms are likely to hire more coworkers as early employees
due to skill similarity. I explore this relationship between previous workplace size and
the representation of coworkers in the founding and early employee teams in the
following subsection.
3.2 Firm Size and Early Team Composition
(1) Empirical Specifications
The baseline equation can be expressed as,
(3)
The dependent variable, , is a fraction. The numerator is the number of either
cofounders or early employees at startup s, who are entrepreneur j’s coworkers at firm w.
The denominator is the total number of entrepreneur j’s coworkers who are working at
startup s. is the size of firm w. The covariates include indicators for entrepreneur
j’s demographic characteristics and employer status at firm w.
An alternative way to construct the dependent variable is to replace the denominator
with the size of founding team or the total number of early employees at startup s,
respectively. However, using this dependent variable relies on the underlying assumption
that entrepreneurs always prefer coworker network to other sources when searching for
cofounders or early employees. This could be due to network constraints or information
asymmetry. However, there are certainly cases in which entrepreneurs prefer to work
with non-coworkers in startups. In those cases, we would expect to see fewer coworkers
in the founding team or among the early employees, which could be mistakenly
interpreted as the evidence of disadvantage faced by large or small firm employees. This
issue can be avoided by focusing on startups that recruit coworkers, which is the purpose
of constructing the current dependent variable.
Another issue that could cause bias in the estimate of again concerns the small firm
effect. Entrepreneurs who worked at small firms may have more coworkers who are
willing to join startups as cofounders or early employees. While examining the
relationship between previous firm size and the fraction of former coworkers working in
startups as cofounders or early employees, it is important to tease out this part of the
effect that might be attributed to differentials in entrepreneurial preference. The
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remaining effect then can be explained by difference in skill composition among
coworkers between large and small firms. Because entrepreneurial preference is
unobserved, I make an initial attempt by constructing a variable that measures
employees' transition rate from their previous workplace w to any startup founded in
1999. Employees can work as either cofounders or early employees in these startups,
which are not necessarily formed by their coworkers at firm w. The idea of using this
variable is to imperfectly capture the general preference for entrepreneurship possessed
by entrepreneur j's former coworkers at firm w.
Moreover, it is reasonable to argue that entrepreneurs are more inclined to work with
former coworkers if their startups are founded in the same industry as prior employers.
Thus, industry similarity is another factor that needs to be considered in the model. The
full regression model is given by
(4)
where is a dummy variable, which equals one if startup s and prior employer w are
in the same industry, and zero if otherwise.5 is the number of workers working in firm
w who left to work for startups in 1999, divided by the size of firm w, .
(2) Results
The analysis is carried out in the base sample consisting of 2,120 entrepreneurs and 1,690
startups. Columns (1) and (2) in table 6 focus on the percentage of founding team
members at startup s who are entrepreneur j’s coworkers. The baseline results reported
in column (1) show that entrepreneurs’ demographic characteristics do not have any
significant impact on the composition of founding team. However, being the employer of
prior workplace is associated with lower density of coworkers in the entrepreneur's
founding team. This result is not surprising, as employers are probably less likely to
team up with employees to found a business. The positive estimated coefficient on
industry similarity is expected since entrepreneurs would value more industry experience
of former coworkers if the startup is in the same industry as entrepreneurs’ prior
employer.
[Insert Table 6 about here]
5 Industry comparison is based on the six-digit level of Danish Industrial Classification.
17
The variable of particular interest is the (log of) size of prior employer w. The OLS
estimate of the coefficient shows that entrepreneurs who worked at small firms are more
likely to recruit coworkers in their startups as cofounders relative to early employees.
This negative relationship is consistent with the argument in the paper that the degree
of skill diversity is higher among coworkers at smaller workplace, which provides
potential employee entrepreneurs with an advantage to seek cofounders among coworkers.
However, this result could also be explained by the negative relationship between firm
size and employees’ entrepreneurial preference in general. To account for difference in
employees’ entrepreneurial propensity across firms, column (2) includes employees'
transition rate to startups. As expected, this variable appears to have a strong and
positive effect on the outcome of interest, indicating that entrepreneurs are more likely
to include former coworkers in the founding team if employees' preference for working for
startups is generally higher at their prior workplace. After including this variable, the
size effect, however, becomes insignificant, although it remains negative. The loss of
significance is mostly due to the high negative correlation between the two variables (-
0.62). As the size variable is now expected to only capture difference in skill composition
among coworkers, the result in column (2) provides weak support for the hypothesis that
skill diversity, which is more common among coworkers at small firms, increases the
likelihood of employee entrepreneurs drawing founding team members from the network
of former coworkers. But this outcome of interest is, by comparison, more influenced by
the extent of entrepreneurial preference exhibited by employees at entrepreneurs’ prior
workplace.
In columns (3) and (4), I replace the dependent variable with the percentage of early
employees at startup s who are entrepreneur j’s former coworkers. Columns (3) and (4)
show that entrepreneurs are more likely to hire former coworkers as early employees, if
they are employers of the previous workplace. One plausible explanation is that
employers are more likely to share similar work value and knowledge with their own
employees than outsiders. Moreover, similar to columns (1) and (2) industry similarity
between startups and prior employers also predicts a higher likelihood that entrepreneurs
would prefer to hire coworkers as early employees in startups, indicating that symmetric
information about coworkers’ industry-specific skills plays a significant role in spinoffs’
recruitment of early employees and cofounders.
18
More important, without controlling for coworkers’ entrepreneurial preference, column (3)
shows that there is no significant difference between large and small firm entrepreneurs
with respect to their propensity to hire coworkers as early employees. This insignificant
relationship could be a joint result of two offsetting effects of firm size: the positive effect
attributed to skill similarity among coworkers at large firms, and the negative effect due
to lower entrepreneurial preference that is often associated with large firm employees. To
take into account the negative effect of firm size, I include in column (4) the variable
that measures employees' transition rate to startups at entrepreneurs' prior workplace.
As in column (2), this variable also predicts higher density of coworkers among early
employees in the startup.
After including this variable, the estimated coefficient on firm size remains positive and
but becomes significant at one percent level. This result suggests that entrepreneurs who
came from large firms are more likely to recruit their former coworkers as early
employees relative to cofounders in new startups. The reason behind this relationship is
that skill similarity is more commonly perceived among coworkers at large firms.
The results presented in table 6 provide additional evidence that entrepreneurs place
more value on skill diversity when selecting founding partners, while emphasize shared
understanding of knowledge and skills when choosing early employees.
4. Conclusions
This paper is motivated by an emerging discussion (among both researchers and
practitioners) on the important role played by early employees in startup formation and
performance (Roach and Sauermann 2013). What makes early employees distinctive
from regular workers is their closer interaction with founders and deeper involvement in
the early stage of business operation. From this perspective, early employees play an
equally important role as founders in building and scaling startups. They are also similar
to founders with respect to the preference for working for small entrepreneurial firms,
but probably fundamentally different from founders in other aspects. Given the special
relationship between early employees and founders, the primary goal of this paper is to
rethink some of the insights about founding team formation that are drawn from
19
previous studies, which rarely take into account the interplay between founders and
early employees.
One of these insights that have been repeatedly presented in the literature is the
relevance of functional diversity on founding team performance. Despite of the perceived
barriers of communication among people with different backgrounds, previous studies
consistently show a positive relationship between founding team diversity and firm
performance. This paper investigates this paradox by comparing skills of cofounders and
early employees, and their match with the entrepreneurs. The interesting finding is that
individuals who have different skills from the entrepreneur are more likely to become
cofounders, and those with the same skills tend to become early employees. This result
suggests that efficiency and competency could be simultaneously achieved in startups
with different skill structures of founding team and early employees. While diversity
appears to be crucial for entrepreneurial decision making, entrepreneurs are inclined to
hire early employees who share the same knowledge as them to ensure the flow of
knowledge transmission and communication.
This paper is also related to an emerging literature, which studies the relationship
between workplace characteristics and employees’ propensity to become entrepreneurs.
The most prominent finding in this literature is the so-called the small firm effect, which
shows a robust negative effect of firm size on the likelihood of employees becoming
entrepreneurs (e.g. Wagner 2004, Dobrev and Barnett 2005, Gompers et al., 2005, Parker
2006, Sørensen 2007, Sørensen and Phillips 2011, Elfenbein et al. 2010, Chen 2012).
Several mechanisms have been proposed to interpret this relationship. The most well-
known ones include self-selection, suggesting that individuals with innate entrepreneurial
attributes (e.g. preference for autonomy) are more likely to work for small firms
(Sørensen 2007). Meanwhile, it is also argued that the nature of small-firm jobs which
are more broadly defined provides employees with peculiar opportunities to engage in a
variety of activities and develop diversified skills that are suitable for entrepreneurship
(Dobrev and Barnett 2005, Gompers et al., 2005, Parker 2009, Werner and Moog 2009,
Elfenbein et al. 2010). Moreover, differences in industry entry barriers facing small
versus large firm employees are also found to account for part of the small firm effect
(Chen 2012).
20
Given the difference in skills required for cofounders and early employees, this paper
further investigates whether the nature of large or small organization affects the
structure of its employees’ local network, particularly the skill composition, which
subsequently defines the pool of potential cofounders and early employees faced by
prospective employee entrepreneurs. The result shows that prospective entrepreneurs
who worked for large firms tend to have more coworkers who are suitable for early
employees. But meanwhile, they are lack of cofounder candidates in the network
developed at the workplace. This finding provides an alternative perspective to
understand why small firm employees are more likely to transition into entrepreneurship
than large firm employees.
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Table 1 Comparing Parent Firms and Startups
Parent Firm Startups
Number of employees 140.3 3.4
Industry
Agriculture 1.33 0.06
Manufacturing 15.21 6.63
Utility 0.36
Construction 15.45 16.64
Wholesale and Retail 35.00 45.49
Trans, Post, and telecommunication 5.97 5.10
Low-Tech Intensive Business Activities 4.95 6.51
Public and Personal Services 11.41 4.60
Knowledge Intensive Business Services 10.32 14.98
Obs. 1,657 1,629
Table 2 Illustration of Data Structure
Individual ID Workplace Startup ID Founder 1 ID Founder 2 ID
1 P1 W1 N001 P2 .
2 P1 W1 N002 P3 P5
3 P2 W1 N001 P2
4 P2 W1 N002 P3 P5
5 P3 W1 N001 P2 .
6 P3 W1 N002 P5 .
7 P5 W1 N001 P2
8 P5 W1 N002 P3
Table 3 Summary Statistics by Employment Choice
Early Employees Founders Others
Age 34.75 34.96 37.91
College 0.06 0.08 0.13
Male 0.65 0.72 0.58
Married 0.42 0.48 0.49
Danish 0.96 0.95 0.96
Number of children under 18 0.60 0.85 0.64
Hourly wage at previous workplace 134.70 155.78 168.10
Tenure at previous workplace 4.28 3.75 5.46
Employer at previous workplace 0.02 0.10 0.003
Self-employed previously 0.03 0.09 0.008
Number of employees at previous workplace 98.79 465.79 4,859.17
Obs. 982 1,954 176,811
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Table 4 What Determines Entrepreneurs’ Selection of Cofounders vs. Early Employees
Logit Multinomial Probit Dept Var = 1
if becoming an early employee Not Join Early
Employees (1) (2) (3) (4)
Prior Employment Characteristics Same Skill 1.425*** 1.997*** 1.443*** 1.000***
(9.01) (6.54) (11.25) (5.99) Firm Size -0.178*** -0.155*** 0.535*** -0.160***
(-6.19) (-5.17) (56.28) (-8.96) Same Skill*Firm Size -0.193** -0.160*** -0.078*
(-2.20) (-5.84) (-1.82) Individual Characteristics Age 0.010* 0.010* 0.010*** 0.005*
(1.90) (1.91) (5.56) (1.71) College 0.152 0.142 0.032 0.105
(0.77) (0.71) (0.56) (1.06) Male -0.484*** -0.490*** -0.295*** -0.259***
(-4.44) (-4.49) (-8.00) (-4.50) Married -0.006 -0.001 -0.101** -0.034
(-0.05) (-0.01) (-2.49) (-0.53) Dane 0.191 0.184 -0.096 0.285**
(0.75) (0.73) (-1.13) (2.01) No. Of Children -0.291*** -0.289*** -0.105*** -0.159***
(-5.34) (-5.30) (-6.13) (-5.50) Hourly Wage -0.006*** -0.006*** -0.001*** -0.002***
(-7.14) (-7.17) (-5.16) (-4.79) Tenure 0.043*** 0.043*** 0.017*** 0.032***
(3.76) (3.72) (4.16) (5.14) Employer -3.034*** -3.092*** -0.505*** -1.609***
(-8.14) (-8.20) (-4.17) (-7.84) Prev. Self-Employed -0.019 -0.007 -0.101 -0.052
(-0.06) (-0.02) (-0.90) (-0.29) Controls for parent firm industry
Y Y Y Y
Ave. Log Likelihood -0.540 -0.539 -0.050 N. 2,645 2,645 197,663
z-scores are in parentheses. Significance levels: ***0.01, **0.05, *0.1.
25
Table 5 Percentages of Early Employees and Cofounders by Firm Size
Firm size Early Employee Cofounder N
25th percentile 35.07 64.93 2,740
50th percentile 5.1 94.9 98
75th percentile 16.13 83.87 93
above 75th percentile 4 96 25
Table 6
How Prior Employer Size Affects the Likelihood of Recruiting Coworkers as Cofounders vs. Early Employees
OLS Regressions Dept Var:
Fraction of Cofounders Dept Var:
Fraction of Early Employees (1) (2) (3) (4)
Prior Employer Characteristics Log Size of Firm -0.018*** -0.004 0.002 0.020***
(-5.39) (-1.06) (0.69) (5.23) Rate of Transition
0.195***
0.244***
(4.28)
(5.73)
Entrepreneur Characteristics Age 0.001 0.001 0.002 0.001
(1.17) (0.53) (1.64) (0.81) College 0.043 0.042 -0.039 -0.039
(1.20) (1.16) (-1.16) (-1.22) Male -0.022 -0.022 0.024 0.023
(-1.11) (-1.16) (1.26) (1.25) Married -0.022 -0.020 -0.004 0.000
(-1.31) (-1.18) (-0.19) (-0.02) Dane 0.041 0.041 0.022 0.021
(1.20) (1.19) (0.61) (0.62) Log of Hourly Wage -0.001 0.016*** -0.045*** -0.024***
(-0.21) (2.78) (-6.39) (-3.27) Tenure 0.000 0.000 0.016*** 0.015***
(0.06) (-0.07) (6.16) (6.11) Employer -0.045** -0.046** 0.086*** 0.084***
(-2.10) (-2.20) (3.28) (3.23) Same Industry 0.056** 0.038 0.186*** 0.163***
(2.10) (1.41) (8.37) (7.30) Controls for Startup Industry Y Y Y Y R2 0.063 0.089 0.197 0.226 N 1928 1928 1928 1928 t statistics are in parentheses. Standard errors are clustered on startups. Significance levels: ***0.01, **0.05, *0.1.